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TABLE 13-6 One of the Most Common Questions of Prospective House Buyers

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TABLE 13-6
One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y) . To provide its customers with information on that matter, a large real estate firm used the following four variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit (X1) , the amount of insulation in inches (X2) , the number of windows in the house (X3) , and the age of the furnace in years (X4) . Given below are the Microsoft Excel outputs of two regression models.
TABLE 13-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y) . To provide its customers with information on that matter, a large real estate firm used the following four variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit (X1) , the amount of insulation in inches (X2) , the number of windows in the house (X3) , and the age of the furnace in years (X4) . Given below are the Microsoft Excel outputs of two regression models.        -Referring to Table 13-6 and allowing for a 1% probability of committing a Type I error, what is the decision and conclusion for the test H₀: β₁ = β₂ = β₃ = β₄ = 0 vs. H₁: At least one βⱼ ≠ 0, j = 1, 2, ..., 4 using Model 1? A)  Do not reject H₀ and conclude that the four independent variables have significant individual linear effects on heating costs. B)  Reject H₀ and conclude that the four independent variables taken as a group have significant linear effects on heating costs. C)  Do not reject H₀ and conclude that the four independent variables taken as a group do not have significant linear effects on heating costs. D)  Reject H₀ and conclude that the four independent variables taken as a group do not have significant linear effects on heating costs.
TABLE 13-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y) . To provide its customers with information on that matter, a large real estate firm used the following four variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit (X1) , the amount of insulation in inches (X2) , the number of windows in the house (X3) , and the age of the furnace in years (X4) . Given below are the Microsoft Excel outputs of two regression models.        -Referring to Table 13-6 and allowing for a 1% probability of committing a Type I error, what is the decision and conclusion for the test H₀: β₁ = β₂ = β₃ = β₄ = 0 vs. H₁: At least one βⱼ ≠ 0, j = 1, 2, ..., 4 using Model 1? A)  Do not reject H₀ and conclude that the four independent variables have significant individual linear effects on heating costs. B)  Reject H₀ and conclude that the four independent variables taken as a group have significant linear effects on heating costs. C)  Do not reject H₀ and conclude that the four independent variables taken as a group do not have significant linear effects on heating costs. D)  Reject H₀ and conclude that the four independent variables taken as a group do not have significant linear effects on heating costs.
-Referring to Table 13-6 and allowing for a 1% probability of committing a Type I error, what is the decision and conclusion for the test H₀: β₁ = β₂ = β₃ = β₄ = 0 vs. H₁: At least one βⱼ ≠ 0, j = 1, 2, ..., 4 using Model 1?


Definitions:

Staircase Analysis

A method for evaluating the progressive steps or stages in a process, such as customer conversion or product development, to identify improvements.

Brand Awareness

The extent to which consumers are familiar with the qualities or image of a particular brand of goods or services.

Market Share

The part of a market that is under the influence of a distinct company or product.

Repeat Customers

Individuals or entities that make purchases from a business on more than one occasion over a period of time.

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